Browsing by Author "Shamsuzzaman, Mohammad"

Now showing 1 - 3 of 3

Application of artificial intelligence as a knowledge creation instrument in tax procedures
(Scopus, 2024-03-30) Koivula, Karri; Shamsuzzoha, Ahm; Shamsuzzaman, Mohammad
This study set out to find whether deep learning algorithms neural networks and self organizing maps could be utilized in a value-adding way in the Finnish Tax Administration in the handling of income tax related claims by limited liability companies. According to research positive outcomes in artificial intelligence (AI) utilization have been attained outside Finland. The research was carried out according to the action design research method in which the focus of the research is concurrently building a suitable artifact for the organization and learning (design principles) from the creation and intervention itself. Research began with problem formulation followed by building, intervention, and evaluation. As a result, the project team consisting of three members created two functional artifacts: one based on neural networks, and another based on self-organizing maps. Creation of the artifacts was done in cycles as alpha, beta and gamma where alpha and beta were a neural network and gamma a self-organizing map. Alpha reached a macro average of 0.75–0.78 in classification and beta 0.77–0.79. Gamma gave a different point of view on the problem and was able to clearly identify the class’s non-estimated customers in a topographical map. The artifacts were limited to function only as knowledge creation instruments due to legal and ethical limitations present in the context. Results suggest that it is recommendable to approach problems with more than one artifact. The preliminary results of this research were validated by applying the concept in a case organization, followed by an analysis of the results in an end-user setting.
Application of Artificial Intelligence as a Knowledge Creation Instrument In Tax Procedures
(Elsevier, 2024-04-25) Koivula, Karri; Shamsuzzoha, AHM; Shamsuzzaman, Mohammad
This study set out to find whether deep learning algorithms neural networks and self-organizing maps could be utilized in a value-adding way in the Finnish Tax Administration in the handling of income tax related claims by limited liability companies. According to research positive outcomes in artificial intelligence (AI) utilization have been attained outside Finland. The research was carried out according to the action design research method in which the focus of the research is concurrently building a suitable artifact for the organization and learning (design principles) from the creation and intervention itself. Research began with problem formulation followed by building, intervention, and evaluation. As a result, the project team consisting of three members created two functional artifacts: one based on neural networks, and another based on self-organizing maps. Creation of the artifacts was done in cycles as alpha, beta and gamma where alpha and beta were a neural network and gamma a self-organizing map. Alpha reached a macro average of 0.75–0.78 in classification and beta 0.77–0.79. Gamma gave a different point of view on the problem and was able to clearly identify the class's non-estimated customers in a topographical map. The artifacts were limited to function only as knowledge creation instruments due to legal and ethical limitations present in the context. Results suggest that it is recommendable to approach problems with more than one artifact. The preliminary results of this research were validated by applying the concept in a case organization, followed by an analysis of the results in an end-user setting.
Optimal Maintenance for a Waste-to-Energy Plant Using DEMATEL: A Case Study
(Springer Nature, 2023-05-04) Igodo, Alassani; Shamsuzzoha, Ahm; Ndzibah, Emmanuel; Shamsuzzaman, Mohammad
"Waste-to-energy (WtE) plants are complex systems that requiring different types of maintenance to be reliable and available in functionality. The inadequacies of WtE plant lifetime maintenance may increase the production costs and negatively affect the competitiveness and the availability of WtE plants. To keep the efficiency of all the plant systems high and operating as expected during their lifetime, it is important to maintain them. This study focuses on the maintenance of WtE plants by analyzing the operating procedures at a case company. In the study, a multi-criterion decision-making method (MCDM) named Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to evaluate the weight and rank of twelve identified criteria for spare parts of the case WtE company. The empirical part of this study consists of a qualitative study, where data were collected from an open-ended questionnaire survey and case company data from existing documents. The respondents' rate from the questionnaire survey was 20%. Key findings from the study show that human, economic, equipment and tool related, management, and environmental factors have an important impact on the effectiveness of the maintenance and availability of the WtE plant. The study also shows that quality, lead time, price, and the severity of spare part failure are the key criteria to consider when selecting spare parts for the WtE plant. The study recommends several initiatives to improve the availability of WtE plant and spare parts which will help to reduce the costs of maintenance as well as mitigate the risks related to the maintenance.